Abstract B014: Massively parallel functional assessment of label-free mutant pools is a universal approach to parametrize mechanistic models of drug resistance evolution
Authors: Haider Inam, Scott Leighow, Justin Pritchard
Journal: Cancer Research (2022)

Abstract Systematically scanning the effects of amino acid substitutions across a protein is a powerful technique in protein engineering, synthetic biology, and the study of drug resistance. In general, these selection experiments increase growth rates to select for mutants. Current methods use molecular barcodes to circumvent the resolution barriers imposed by conventional NGS sequencing of point mutations in mutant pools. Barcodes introduce synonymous mutations in the genome and assume neutrality, but it is now well accepted that synonymous mutations affect protein function. In a different approach, barcodes on cDNA constructs require a cumbersome second cloning step during library generation. We describe an approach that harnesses a highly sensitive duplex sequencing strategy to skip this barcode integration step and directly measure the target in label-free deep mutant pools. Furthermore, we employ the use of single-mutant standards with well-studied growth dynamics. By tracking the deviations from the expected growth trajectory of these mutant standards, we correct for dose-response variations that often result in low replicate agreement in these functional screens. Our workflow is compatible with any pool generation strategy, and the parameters that we obtain are robust enough to be useful in detailed models of cellular dynamics in synthetic biology studies. We developed a method for the functional assessment of deep mutant pools in a single pooled experiment. The workflow includes generating a label-free library of mutants at a desired target and sequencing the mutagenized pool before and during drug treatment using duplex sequencing. We demonstrate our pooled approach by focusing on target-driven resistance in Chronic Myeloid Leukemia (CML). To this end, we studied the 17 most clinically abundant BCRABL resistant mutations in imatinib refractory CML at low frequencies of 1 in 15,000. The sensitivity afforded by duplex sequencing enabled the accurate quantification of growth rates of these mutants under imatinib selection, with all 17 mutants closely matching their expected growth trajectory as predicted by individual dose-response studies. Moreover, we use an inference-based approach that uses hill-curve information from our mutant standards to detect slight variations in attempted dose between pooled replicates. Correcting for these slight variations in attempted dose leads to a &amp;gt;10-fold improvement in replicate agreement. Here, we demonstrate a fast, label-free workflow that achieves high levels of sensitivity and specificity in measuring growth dynamics of deep mutant pools. Importantly, our method does not rely on synonymous, potentially error-inducing labels. Given recent successes in deep learning that predict structure and function in proteins, our approach can be used to generate large training sets of mutation-drug phenotypes. Predicting the rich phenotypic data generated using existing sequence and structural data could dramatically reduce the experiments that are necessary to generate predictive information on drug resistance. Citation Format: Haider Inam, Scott Leighow, Justin Pritchard. Massively parallel functional assessment of label-free mutant pools is a universal approach to parametrize mechanistic models of drug resistance evolution [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr B014.